Classification is a fundamental statistical problem in data science, with applications ranging from genomics to character recognition. AdaBoost, proposed by Freund and Schapire [1]} and further developed in [2]}, is a popular and successful algorithm from the machine learning literature to tackle such classifications problems. It is based on building an additive model with coefficients \(\tilde{\beta }_T\)  composed of simple classifiers such as regression trees and then using the binary classification rule \(\textnormal {sgn}(\langle \tilde{\beta }_T, \cdot \rangle )\) . At each iteration another simple classifier is added to the model, minimizing a weighted loss-function. Alternatively, AdaBoost can be viewed as a variant of mirror-gradient-descent for the exponential loss [3]}, [4]}. Empirically, it often achieves the best generalization performance when it is overparameterized and runs long after the training error equals zero [5]}.
